Storage Efficient Particle Filters with Multiple Out-of-Sequence Measurements
(2012) 15th International Conference on Information Fusion p.471-478- Abstract
- A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2837293
- author
- Berntorp, Karl LU ; Årzén, Karl-Erik LU and Robertsson, Anders LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 15th International Conference on Information Fusion (FUSION), 2012
- pages
- 471 - 478
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 15th International Conference on Information Fusion
- conference location
- Singapore
- conference dates
- 2012-07-09
- external identifiers
-
- scopus:84867637663
- ISBN
- 978-1-4673-0417-7
- project
- ENGROSS
- language
- English
- LU publication?
- yes
- id
- 4527a1d5-cf21-43c4-a353-8a544541a02c (old id 2837293)
- date added to LUP
- 2016-04-04 12:13:22
- date last changed
- 2024-06-09 01:48:06
@inproceedings{4527a1d5-cf21-43c4-a353-8a544541a02c, abstract = {{A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements.}}, author = {{Berntorp, Karl and Årzén, Karl-Erik and Robertsson, Anders}}, booktitle = {{15th International Conference on Information Fusion (FUSION), 2012}}, isbn = {{978-1-4673-0417-7}}, language = {{eng}}, pages = {{471--478}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Storage Efficient Particle Filters with Multiple Out-of-Sequence Measurements}}, url = {{https://lup.lub.lu.se/search/files/5955958/3450810.pdf}}, year = {{2012}}, }